From 3e694305f9f1b2a1115bce2d41b9fd166930f9cc Mon Sep 17 00:00:00 2001 From: Lucas Oliveira <62335616+lucaseduoli@users.noreply.github.com> Date: Fri, 29 May 2026 15:33:49 -0300 Subject: [PATCH 1/3] fix: separate docling chunking dependencies (#13411) * separate docling chunking * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * fix nitpicks * [autofix.ci] apply automated fixes * remove langchain docling dependency * add docling image description as an optional dep * [autofix.ci] apply automated fixes * fix line too long * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> --- docs/docs/Components/bundles-docling.mdx | 5 +- pyproject.toml | 12 +- .../starter_projects/Knowledge Retrieval.json | 2 +- .../starter_projects/Vector Store RAG.json | 2 +- src/backend/base/pyproject.toml | 12 +- .../test_chunk_docling_document_component.py | 36 +++- src/lfx/src/lfx/_assets/component_index.json | 26 +-- src/lfx/src/lfx/base/data/docling_utils.py | 10 +- .../docling/chunk_docling_document.py | 51 ++--- .../lfx/components/docling/docling_inline.py | 16 +- uv.lock | 182 +++++++++++++----- 11 files changed, 250 insertions(+), 104 deletions(-) diff --git a/docs/docs/Components/bundles-docling.mdx b/docs/docs/Components/bundles-docling.mdx index 16d750d186..df675756a2 100644 --- a/docs/docs/Components/bundles-docling.mdx +++ b/docs/docs/Components/bundles-docling.mdx @@ -25,8 +25,9 @@ The Docling dependency is required to use the Docling components in Langflow. * **Langflow Desktop**: Set `LANGFLOW_DOCLING=True` in your `.env` file to enable Docling dependency installation. For more information, see [Set environment variables for Langflow Desktop](/environment-variables#set-environment-variables-for-langflow-desktop). * **Langflow OSS**: - * If you installed `langflow` (`uv pip install langflow`), Docling is included automatically through bundled extras. + * If you installed `langflow` (`uv pip install langflow`), Docling is included automatically through bundled extras, without Docling's optional chunking dependencies. * If you installed `langflow-base` directly, install Docling with an extra, for example `uv pip install "langflow-base[docling]"`. + * To use the **Chunk DoclingDocument** component, install the chunking extra with `uv pip install "langflow[docling-chunking]"` or `uv pip install "langflow-base[docling-chunking]"`. * **macOS Intel (x86_64)**: Use the [Docling installation guide](https://docling-project.github.io/docling/installation/) to install the Docling dependency. @@ -103,6 +104,8 @@ For more information, see the [Docling serve project repository](https://github. The **Chunk DoclingDocument** component splits `DoclingDocument` objects into chunks. +This component requires Docling's optional chunking dependencies. Install them with `uv pip install "langflow[docling-chunking]"` or `uv pip install "langflow-base[docling-chunking]"`. + It outputs the chunked documents as a [`Table`](/data-types#table). For more information, see the [Docling core project repository](https://github.com/docling-project/docling-core). diff --git a/pyproject.toml b/pyproject.toml index 2131f60390..8c2a219ebd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -92,7 +92,7 @@ Documentation = "https://docs.langflow.org" [project.optional-dependencies] docling = [ - "langchain-docling>=1.1.0", + "langflow-base[docling]>=0.9.5", "tesserocr>=2.8.0", "rapidocr-onnxruntime>=1.4.4", "ocrmac>=1.0.0; sys_platform == 'darwin'", @@ -103,6 +103,16 @@ docling = [ "torchvision>=0.21.0", # torchvision 0.21+ is compatible with PyTorch 2.6+ ] +docling-chunking = [ + "langflow-base[docling-chunking]>=0.9.5", +] + +docling-image-description = [ + "langflow-base[docling-image-description]>=0.9.5", + "langchain-docling>=1.1.0", +] + + audio = [ "webrtcvad>=2.0.10", ] diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json b/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json index 87d502804f..bf7a42937e 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json @@ -585,7 +585,7 @@ }, { "name": "langchain_ibm", - "version": null + "version": "1.0.11" } ], "total_dependencies": 12 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json index b4616f1597..264f419c60 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json @@ -3097,7 +3097,7 @@ }, { "name": "langchain_ibm", - "version": null + "version": "1.0.11" } ], "total_dependencies": 12 diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index 4773730faf..22d26e48c1 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -327,8 +327,18 @@ openlayer = ["openlayer>=0.9.0,<1.0.0"] # Document processing / OCR docling = [ "docling-core>=2.77.0,<3.0.0", - "docling>=2.36.1,<3.0.0; sys_platform != 'darwin' or platform_machine != 'x86_64'", + "docling-slim[cli,convert-core,extract-core,feat-ocr-rapidocr,format-latex,format-office,format-pdf,format-web,models-local,service-client]>=2.36.1,<3.0.0; sys_platform != 'darwin' or platform_machine != 'x86_64'", ] +docling-chunking = [ + "langflow-base[docling]", + "docling-core[chunking]>=2.77.0,<3.0.0", + "tiktoken>=0.7.0", +] +docling-image-description = [ + "langflow-base[docling]", + "langchain-docling>=1.1.0", +] + easyocr = ["easyocr>=1.7.2,<2.0.0; sys_platform != 'darwin' or platform_machine != 'x86_64'"] # Additional tools diff --git a/src/backend/tests/unit/components/docling/test_chunk_docling_document_component.py b/src/backend/tests/unit/components/docling/test_chunk_docling_document_component.py index ca2928f7b0..5d13fa805b 100644 --- a/src/backend/tests/unit/components/docling/test_chunk_docling_document_component.py +++ b/src/backend/tests/unit/components/docling/test_chunk_docling_document_component.py @@ -1,14 +1,14 @@ """Tests for ChunkDoclingDocumentComponent HybridChunker parameters.""" +import builtins import sys import types import pytest - -pytest.importorskip("tiktoken") -pytest.importorskip("docling_core") - -from lfx.components.docling.chunk_docling_document import ChunkDoclingDocumentComponent +from lfx.components.docling.chunk_docling_document import ( + ChunkDoclingDocumentComponent, + _load_docling_chunker_dependencies, +) def _base_build_config(): @@ -95,6 +95,11 @@ class TestChunkDoclingDocumentComponentHybridChunker: captured["max_tokens"] = max_tokens return "tokenizer" + class DummyDocMeta: + @classmethod + def model_validate(cls, meta): + return meta + hybrid_chunker_module = types.ModuleType("docling_core.transforms.chunker.hybrid_chunker") hybrid_chunker_module.HybridChunker = DummyHybridChunker monkeypatch.setitem(sys.modules, "docling_core.transforms.chunker.hybrid_chunker", hybrid_chunker_module) @@ -110,8 +115,8 @@ class TestChunkDoclingDocumentComponentHybridChunker: huggingface_tokenizer_module, ) monkeypatch.setattr( - "lfx.components.docling.chunk_docling_document.HierarchicalChunker", - DummyHierarchicalChunker, + "lfx.components.docling.chunk_docling_document._load_docling_chunker_dependencies", + lambda: (DummyDocMeta, DummyHierarchicalChunker), ) monkeypatch.setattr( "lfx.components.docling.chunk_docling_document.extract_docling_documents", @@ -182,3 +187,20 @@ class TestChunkDoclingDocumentComponentHybridChunker: always_emit_headings_input=True, ) assert captured["hierarchical_called"] is True + + def test_missing_chunking_extra_has_actionable_error(self, monkeypatch): + original_import = builtins.__import__ + + def fake_import(name, globals_=None, locals_=None, fromlist=(), level=0): + if name in { + "docling_core.transforms.chunker.doc_chunk", + "docling_core.transforms.chunker.hierarchical_chunker", + }: + msg = "Module requires 'chunking' extra" + raise RuntimeError(msg) + return original_import(name, globals_, locals_, fromlist, level) + + monkeypatch.setattr(builtins, "__import__", fake_import) + + with pytest.raises(ImportError, match=r"langflow-base\[docling-chunking\]"): + _load_docling_chunker_dependencies() diff --git a/src/lfx/src/lfx/_assets/component_index.json b/src/lfx/src/lfx/_assets/component_index.json index c592123bef..9600ad5972 100644 --- a/src/lfx/src/lfx/_assets/component_index.json +++ b/src/lfx/src/lfx/_assets/component_index.json @@ -58321,7 +58321,7 @@ }, { "name": "markitdown", - "version": "0.1.5" + "version": "0.1.6" }, { "name": "lfx", @@ -64195,20 +64195,20 @@ "icon": "Docling", "legacy": false, "metadata": { - "code_hash": "7775393185fe", + "code_hash": "a6a57b9f497d", "dependencies": { "dependencies": [ { - "name": "tiktoken", - "version": "0.13.0" + "name": "lfx", + "version": null }, { "name": "docling_core", "version": "2.77.1" }, { - "name": "lfx", - "version": null + "name": "tiktoken", + "version": "0.13.0" } ], "total_dependencies": 3 @@ -64303,7 +64303,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import json\n\nimport tiktoken\nfrom docling_core.transforms.chunker import BaseChunker, DocMeta\nfrom docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker\n\nfrom lfx.base.data.docling_utils import extract_docling_documents\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MessageTextInput, Output, StrInput\nfrom lfx.schema import Data, DataFrame\n\n\nclass ChunkDoclingDocumentComponent(Component):\n display_name: str = \"Chunk DoclingDocument\"\n description: str = \"Use the DocumentDocument chunkers to split the document into chunks.\"\n documentation = \"https://docling-project.github.io/docling/concepts/chunking/\"\n icon = \"Docling\"\n name = \"ChunkDoclingDocument\"\n\n inputs = [\n HandleInput(\n name=\"data_inputs\",\n display_name=\"JSON or Table\",\n info=\"The data with documents to split in chunks.\",\n input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n required=True,\n ),\n DropdownInput(\n name=\"chunker\",\n display_name=\"Chunker\",\n options=[\"HybridChunker\", \"HierarchicalChunker\"],\n info=(\"Which chunker to use.\"),\n value=\"HybridChunker\",\n real_time_refresh=True,\n input_types=[\"Message\"],\n ),\n DropdownInput(\n name=\"provider\",\n display_name=\"Provider\",\n options=[\"Hugging Face\", \"OpenAI\"],\n info=(\"Which tokenizer provider.\"),\n value=\"Hugging Face\",\n show=True,\n real_time_refresh=True,\n advanced=True,\n dynamic=True,\n ),\n StrInput(\n name=\"hf_model_name\",\n display_name=\"HF model name\",\n info=(\n \"Model name of the tokenizer to use with the HybridChunker when Hugging Face is chosen as a tokenizer.\"\n ),\n value=\"sentence-transformers/all-MiniLM-L6-v2\",\n show=True,\n advanced=True,\n dynamic=True,\n ),\n StrInput(\n name=\"openai_model_name\",\n display_name=\"OpenAI model name\",\n info=(\"Model name of the tokenizer to use with the HybridChunker when OpenAI is chosen as a tokenizer.\"),\n value=\"gpt-4o\",\n show=False,\n advanced=True,\n dynamic=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Maximum tokens\",\n info=(\"Maximum number of tokens for the HybridChunker.\"),\n show=True,\n required=False,\n advanced=True,\n dynamic=True,\n input_types=[\"Message\"],\n ),\n BoolInput(\n name=\"merge_peers\",\n display_name=\"Merge peers\",\n info=\"Merge undersized chunks sharing the same relevant metadata.\",\n value=True,\n show=True,\n advanced=True,\n dynamic=True,\n ),\n BoolInput(\n name=\"always_emit_headings\",\n display_name=\"Always emit headings\",\n info=\"Emit headings even for empty sections.\",\n value=False,\n show=True,\n advanced=True,\n dynamic=True,\n ),\n MessageTextInput(\n name=\"doc_key\",\n display_name=\"Doc Key\",\n info=\"The key to use for the DoclingDocument column.\",\n value=\"doc\",\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Table\", name=\"dataframe\", method=\"chunk_documents\"),\n ]\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n \"\"\"Update build_config to show/hide fields based on chunker and provider selection.\"\"\"\n if field_name == \"chunker\":\n provider_type = build_config[\"provider\"][\"value\"]\n is_hf = provider_type == \"Hugging Face\"\n is_openai = provider_type == \"OpenAI\"\n if field_value == \"HybridChunker\":\n build_config[\"provider\"][\"show\"] = True\n build_config[\"hf_model_name\"][\"show\"] = is_hf\n build_config[\"openai_model_name\"][\"show\"] = is_openai\n build_config[\"max_tokens\"][\"show\"] = True\n build_config[\"merge_peers\"][\"show\"] = True\n build_config[\"always_emit_headings\"][\"show\"] = True\n else:\n build_config[\"provider\"][\"show\"] = False\n build_config[\"hf_model_name\"][\"show\"] = False\n build_config[\"openai_model_name\"][\"show\"] = False\n build_config[\"max_tokens\"][\"show\"] = False\n build_config[\"merge_peers\"][\"show\"] = False\n build_config[\"always_emit_headings\"][\"show\"] = False\n elif field_name == \"provider\" and build_config[\"chunker\"][\"value\"] == \"HybridChunker\":\n if field_value == \"Hugging Face\":\n build_config[\"hf_model_name\"][\"show\"] = True\n build_config[\"openai_model_name\"][\"show\"] = False\n elif field_value == \"OpenAI\":\n build_config[\"hf_model_name\"][\"show\"] = False\n build_config[\"openai_model_name\"][\"show\"] = True\n\n return build_config\n\n def _docs_to_data(self, docs) -> list[Data]:\n return [Data(text=doc.page_content, data=doc.metadata) for doc in docs]\n\n def chunk_documents(self) -> DataFrame:\n documents, warning = extract_docling_documents(self.data_inputs, self.doc_key)\n if warning:\n self.status = warning\n\n chunker: BaseChunker\n if self.chunker == \"HybridChunker\":\n try:\n from docling_core.transforms.chunker.hybrid_chunker import HybridChunker\n except ImportError as e:\n msg = (\n \"HybridChunker is not installed. Please install it with `uv pip install docling-core[chunking] \"\n \"or `uv pip install transformers`\"\n )\n raise ImportError(msg) from e\n max_tokens: int | None = self.max_tokens if self.max_tokens else None\n if self.provider == \"Hugging Face\":\n try:\n from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer\n except ImportError as e:\n msg = (\n \"HuggingFaceTokenizer is not installed.\"\n \" Please install it with `uv pip install docling-core[chunking]`\"\n )\n raise ImportError(msg) from e\n tokenizer = HuggingFaceTokenizer.from_pretrained(\n model_name=self.hf_model_name,\n max_tokens=max_tokens,\n )\n elif self.provider == \"OpenAI\":\n try:\n from docling_core.transforms.chunker.tokenizer.openai import OpenAITokenizer\n except ImportError as e:\n msg = (\n \"OpenAITokenizer is not installed.\"\n \" Please install it with `uv pip install docling-core[chunking]`\"\n \" or `uv pip install transformers`\"\n )\n raise ImportError(msg) from e\n if max_tokens is None:\n max_tokens = 128 * 1024 # context window length required for OpenAI tokenizers\n tokenizer = OpenAITokenizer(\n tokenizer=tiktoken.encoding_for_model(self.openai_model_name), max_tokens=max_tokens\n )\n chunker = HybridChunker(\n tokenizer=tokenizer,\n merge_peers=bool(self.merge_peers),\n always_emit_headings=bool(self.always_emit_headings),\n )\n\n elif self.chunker == \"HierarchicalChunker\":\n chunker = HierarchicalChunker()\n else:\n msg = f\"Unknown chunker: {self.chunker}\"\n raise ValueError(msg)\n\n results: list[Data] = []\n try:\n for doc in documents:\n for chunk in chunker.chunk(dl_doc=doc):\n enriched_text = chunker.contextualize(chunk=chunk)\n meta = DocMeta.model_validate(chunk.meta)\n\n results.append(\n Data(\n data={\n \"text\": enriched_text,\n \"document_id\": f\"{doc.origin.binary_hash}\",\n \"doc_items\": json.dumps([item.self_ref for item in meta.doc_items]),\n }\n )\n )\n\n except Exception as e:\n msg = f\"Error splitting text: {e}\"\n raise TypeError(msg) from e\n\n return DataFrame(results)\n" + "value": "import json\nfrom typing import Any\n\nfrom lfx.base.data.docling_utils import extract_docling_documents\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MessageTextInput, Output, StrInput\nfrom lfx.schema import Data, DataFrame\n\n_CHUNKING_INSTALL_HINT = (\n \"Install them with `uv pip install 'langflow[docling-chunking]'`, \"\n \"`uv pip install 'langflow-base[docling-chunking]'`, or \"\n \"`uv pip install 'docling-core[chunking]' tiktoken`.\"\n)\n\n\ndef _load_docling_chunker_dependencies() -> tuple[type[Any], type[Any]]:\n try:\n from docling_core.transforms.chunker.doc_chunk import DocMeta as DocMetaCls\n from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker as HierarchicalChunkerCls\n except (ImportError, RuntimeError) as e:\n msg = f\"Docling chunking dependencies are not installed. {_CHUNKING_INSTALL_HINT}\"\n raise ImportError(msg) from e\n return DocMetaCls, HierarchicalChunkerCls\n\n\nclass ChunkDoclingDocumentComponent(Component):\n display_name: str = \"Chunk DoclingDocument\"\n description: str = \"Use the DocumentDocument chunkers to split the document into chunks.\"\n documentation = \"https://docling-project.github.io/docling/concepts/chunking/\"\n icon = \"Docling\"\n name = \"ChunkDoclingDocument\"\n\n inputs = [\n HandleInput(\n name=\"data_inputs\",\n display_name=\"JSON or Table\",\n info=\"The data with documents to split in chunks.\",\n input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n required=True,\n ),\n DropdownInput(\n name=\"chunker\",\n display_name=\"Chunker\",\n options=[\"HybridChunker\", \"HierarchicalChunker\"],\n info=(\"Which chunker to use.\"),\n value=\"HybridChunker\",\n real_time_refresh=True,\n input_types=[\"Message\"],\n ),\n DropdownInput(\n name=\"provider\",\n display_name=\"Provider\",\n options=[\"Hugging Face\", \"OpenAI\"],\n info=(\"Which tokenizer provider.\"),\n value=\"Hugging Face\",\n show=True,\n real_time_refresh=True,\n advanced=True,\n dynamic=True,\n ),\n StrInput(\n name=\"hf_model_name\",\n display_name=\"HF model name\",\n info=(\n \"Model name of the tokenizer to use with the HybridChunker when Hugging Face is chosen as a tokenizer.\"\n ),\n value=\"sentence-transformers/all-MiniLM-L6-v2\",\n show=True,\n advanced=True,\n dynamic=True,\n ),\n StrInput(\n name=\"openai_model_name\",\n display_name=\"OpenAI model name\",\n info=(\"Model name of the tokenizer to use with the HybridChunker when OpenAI is chosen as a tokenizer.\"),\n value=\"gpt-4o\",\n show=False,\n advanced=True,\n dynamic=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Maximum tokens\",\n info=(\"Maximum number of tokens for the HybridChunker.\"),\n show=True,\n required=False,\n advanced=True,\n dynamic=True,\n input_types=[\"Message\"],\n ),\n BoolInput(\n name=\"merge_peers\",\n display_name=\"Merge peers\",\n info=\"Merge undersized chunks sharing the same relevant metadata.\",\n value=True,\n show=True,\n advanced=True,\n dynamic=True,\n ),\n BoolInput(\n name=\"always_emit_headings\",\n display_name=\"Always emit headings\",\n info=\"Emit headings even for empty sections.\",\n value=False,\n show=True,\n advanced=True,\n dynamic=True,\n ),\n MessageTextInput(\n name=\"doc_key\",\n display_name=\"Doc Key\",\n info=\"The key to use for the DoclingDocument column.\",\n value=\"doc\",\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Table\", name=\"dataframe\", method=\"chunk_documents\"),\n ]\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n \"\"\"Update build_config to show/hide fields based on chunker and provider selection.\"\"\"\n if field_name == \"chunker\":\n provider_type = build_config[\"provider\"][\"value\"]\n is_hf = provider_type == \"Hugging Face\"\n is_openai = provider_type == \"OpenAI\"\n if field_value == \"HybridChunker\":\n build_config[\"provider\"][\"show\"] = True\n build_config[\"hf_model_name\"][\"show\"] = is_hf\n build_config[\"openai_model_name\"][\"show\"] = is_openai\n build_config[\"max_tokens\"][\"show\"] = True\n build_config[\"merge_peers\"][\"show\"] = True\n build_config[\"always_emit_headings\"][\"show\"] = True\n else:\n build_config[\"provider\"][\"show\"] = False\n build_config[\"hf_model_name\"][\"show\"] = False\n build_config[\"openai_model_name\"][\"show\"] = False\n build_config[\"max_tokens\"][\"show\"] = False\n build_config[\"merge_peers\"][\"show\"] = False\n build_config[\"always_emit_headings\"][\"show\"] = False\n elif field_name == \"provider\" and build_config[\"chunker\"][\"value\"] == \"HybridChunker\":\n if field_value == \"Hugging Face\":\n build_config[\"hf_model_name\"][\"show\"] = True\n build_config[\"openai_model_name\"][\"show\"] = False\n elif field_value == \"OpenAI\":\n build_config[\"hf_model_name\"][\"show\"] = False\n build_config[\"openai_model_name\"][\"show\"] = True\n\n return build_config\n\n def _docs_to_data(self, docs) -> list[Data]:\n return [Data(text=doc.page_content, data=doc.metadata) for doc in docs]\n\n def chunk_documents(self) -> DataFrame:\n documents, warning = extract_docling_documents(self.data_inputs, self.doc_key)\n if warning:\n self.status = warning\n\n doc_meta_cls, hierarchical_chunker_cls = _load_docling_chunker_dependencies()\n chunker: Any\n if self.chunker == \"HybridChunker\":\n try:\n from docling_core.transforms.chunker.hybrid_chunker import HybridChunker\n except (ImportError, RuntimeError) as e:\n msg = f\"HybridChunker is not installed. {_CHUNKING_INSTALL_HINT}\"\n raise ImportError(msg) from e\n max_tokens: int | None = self.max_tokens if self.max_tokens else None\n if self.provider == \"Hugging Face\":\n try:\n from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer\n except (ImportError, RuntimeError) as e:\n msg = f\"HuggingFaceTokenizer is not installed. {_CHUNKING_INSTALL_HINT}\"\n raise ImportError(msg) from e\n tokenizer = HuggingFaceTokenizer.from_pretrained(\n model_name=self.hf_model_name,\n max_tokens=max_tokens,\n )\n elif self.provider == \"OpenAI\":\n try:\n import tiktoken\n from docling_core.transforms.chunker.tokenizer.openai import OpenAITokenizer\n except (ImportError, RuntimeError) as e:\n msg = f\"OpenAITokenizer is not installed. {_CHUNKING_INSTALL_HINT}\"\n raise ImportError(msg) from e\n if max_tokens is None:\n max_tokens = 128 * 1024 # context window length required for OpenAI tokenizers\n tokenizer = OpenAITokenizer(\n tokenizer=tiktoken.encoding_for_model(self.openai_model_name), max_tokens=max_tokens\n )\n chunker = HybridChunker(\n tokenizer=tokenizer,\n merge_peers=bool(self.merge_peers),\n always_emit_headings=bool(self.always_emit_headings),\n )\n\n elif self.chunker == \"HierarchicalChunker\":\n chunker = hierarchical_chunker_cls()\n else:\n msg = f\"Unknown chunker: {self.chunker}\"\n raise ValueError(msg)\n\n results: list[Data] = []\n try:\n for doc in documents:\n for chunk in chunker.chunk(dl_doc=doc):\n enriched_text = chunker.contextualize(chunk=chunk)\n meta = doc_meta_cls.model_validate(chunk.meta)\n\n results.append(\n Data(\n data={\n \"text\": enriched_text,\n \"document_id\": f\"{doc.origin.binary_hash}\",\n \"doc_items\": json.dumps([item.self_ref for item in meta.doc_items]),\n }\n )\n )\n\n except Exception as e:\n msg = f\"Error splitting text: {e}\"\n raise TypeError(msg) from e\n\n return DataFrame(results)\n" }, "data_inputs": { "_input_type": "HandleInput", @@ -64500,7 +64500,7 @@ "icon": "Docling", "legacy": false, "metadata": { - "code_hash": "237cc5f76c44", + "code_hash": "2ccfca344d7a", "dependencies": { "dependencies": [ { @@ -64553,7 +64553,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import json\nimport subprocess\nimport sys\nimport textwrap\nimport time\n\nfrom lfx.base.data import BaseFileComponent\nfrom lfx.base.data.docling_utils import _serialize_pydantic_model\nfrom lfx.inputs import BoolInput, DropdownInput, HandleInput, StrInput\nfrom lfx.schema import Data\n\n\nclass DoclingInlineComponent(BaseFileComponent):\n display_name = \"Docling\"\n description = \"Uses Docling to process input documents running the Docling models locally.\"\n documentation = \"https://docling-project.github.io/docling/\"\n trace_type = \"tool\"\n icon = \"Docling\"\n name = \"DoclingInline\"\n\n # https://docling-project.github.io/docling/usage/supported_formats/\n VALID_EXTENSIONS = [\n \"adoc\",\n \"asciidoc\",\n \"asc\",\n \"bmp\",\n \"csv\",\n \"dotx\",\n \"dotm\",\n \"docm\",\n \"docx\",\n \"htm\",\n \"html\",\n \"jpeg\",\n \"json\",\n \"md\",\n \"pdf\",\n \"png\",\n \"potx\",\n \"ppsx\",\n \"pptm\",\n \"potm\",\n \"ppsm\",\n \"pptx\",\n \"tiff\",\n \"txt\",\n \"xls\",\n \"xlsx\",\n \"xhtml\",\n \"xml\",\n \"webp\",\n ]\n\n inputs = [\n *BaseFileComponent.get_base_inputs(),\n DropdownInput(\n name=\"pipeline\",\n display_name=\"Pipeline\",\n info=\"Docling pipeline to use\",\n options=[\"standard\", \"vlm\"],\n value=\"standard\",\n ),\n DropdownInput(\n name=\"ocr_engine\",\n display_name=\"OCR Engine\",\n info=\"OCR engine to use. None will disable OCR.\",\n options=[\"None\", \"easyocr\", \"tesserocr\", \"rapidocr\", \"ocrmac\"],\n value=\"None\",\n ),\n BoolInput(\n name=\"do_picture_classification\",\n display_name=\"Picture classification\",\n info=\"If enabled, the Docling pipeline will classify the pictures type.\",\n value=False,\n ),\n HandleInput(\n name=\"pic_desc_llm\",\n display_name=\"Picture description LLM\",\n info=\"If connected, the model to use for running the picture description task.\",\n input_types=[\"LanguageModel\"],\n required=False,\n ),\n StrInput(\n name=\"pic_desc_prompt\",\n display_name=\"Picture description prompt\",\n value=\"Describe the image in three sentences. Be concise and accurate.\",\n info=\"The user prompt to use when invoking the model.\",\n advanced=True,\n ),\n # TODO: expose more Docling options\n ]\n\n outputs = [\n *BaseFileComponent.get_base_outputs(),\n ]\n\n # ------------------------------------------------------------------ #\n # Child script that runs Docling in a separate OS process. #\n # Uses subprocess.Popen (same pattern as Read File advanced mode) #\n # instead of multiprocessing/threading so that: #\n # 1. It works reliably under Gunicorn's fork-based workers #\n # 2. The parent's event loop stays free for SSE heartbeats #\n # 3. No pickling / signal-handler conflicts #\n # ------------------------------------------------------------------ #\n _CHILD_SCRIPT: str = textwrap.dedent(r\"\"\"\n import json, sys\n\n def main():\n cfg = json.loads(sys.stdin.read())\n file_paths = cfg[\"file_paths\"]\n pipeline = cfg[\"pipeline\"]\n ocr_engine = cfg[\"ocr_engine\"]\n do_picture_cls = cfg[\"do_picture_classification\"]\n pic_desc_config = cfg.get(\"pic_desc_config\")\n pic_desc_prompt = cfg.get(\"pic_desc_prompt\", \"\")\n\n try:\n from docling.datamodel.base_models import ConversionStatus, InputFormat\n from docling.datamodel.pipeline_options import PdfPipelineOptions\n from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption\n except ImportError as e:\n print(json.dumps({\"ok\": False, \"error\": f\"Docling is not installed: {e}\"}))\n return\n\n # --- build converter ------------------------------------------------\n try:\n pipe = PdfPipelineOptions()\n pipe.do_ocr = ocr_engine not in (\"\", \"None\")\n if pipe.do_ocr:\n try:\n from docling.models.factories import get_ocr_factory\n fac = get_ocr_factory(allow_external_plugins=False)\n pipe.ocr_options = fac.create_options(kind=ocr_engine)\n except Exception:\n pipe.do_ocr = False\n\n pipe.do_picture_classification = do_picture_cls\n\n if pic_desc_config:\n try:\n import importlib\n from pydantic import TypeAdapter\n from langchain_docling.picture_description import (\n PictureDescriptionLangChainOptions,\n )\n mod_name, cls_name = pic_desc_config[\"__class_path__\"].rsplit(\".\", 1)\n mod = importlib.import_module(mod_name)\n cls = getattr(mod, cls_name)\n adapter = TypeAdapter(cls)\n llm = adapter.validate_python(pic_desc_config[\"config\"])\n pipe.do_picture_description = True\n pipe.allow_external_plugins = True\n pipe.picture_description_options = PictureDescriptionLangChainOptions(\n llm=llm, prompt=pic_desc_prompt,\n )\n except Exception as e:\n print(json.dumps({\"ok\": False, \"error\": f\"Picture description setup failed: {e}\"}))\n return\n\n if pipeline == \"vlm\":\n try:\n from docling.datamodel.pipeline_options import VlmPipelineOptions\n from docling.pipeline.vlm_pipeline import VlmPipeline\n vlm_opts = VlmPipelineOptions()\n if sys.platform == \"darwin\":\n try:\n from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX\n vlm_opts.vlm_options = GRANITEDOCLING_MLX\n except ImportError:\n from docling.datamodel.vlm_model_specs import GRANITEDOCLING_TRANSFORMERS\n vlm_opts.vlm_options = GRANITEDOCLING_TRANSFORMERS\n fmt = {}\n if hasattr(InputFormat, \"PDF\"):\n fmt[InputFormat.PDF] = PdfFormatOption(\n pipeline_cls=VlmPipeline, pipeline_options=vlm_opts,\n )\n if hasattr(InputFormat, \"IMAGE\"):\n fmt[InputFormat.IMAGE] = PdfFormatOption(\n pipeline_cls=VlmPipeline, pipeline_options=vlm_opts,\n )\n converter = DocumentConverter(format_options=fmt)\n except Exception as e:\n print(json.dumps({\"ok\": False, \"error\": f\"VLM pipeline setup failed: {e}\"}))\n return\n else:\n pdf_opt = PdfFormatOption(pipeline_options=pipe)\n fmt = {}\n if hasattr(InputFormat, \"PDF\"):\n fmt[InputFormat.PDF] = pdf_opt\n if hasattr(InputFormat, \"IMAGE\"):\n fmt[InputFormat.IMAGE] = pdf_opt\n converter = DocumentConverter(format_options=fmt)\n except Exception as e:\n print(json.dumps({\"ok\": False, \"error\": f\"Converter creation failed: {e}\"}))\n return\n\n # --- process files --------------------------------------------------\n results = []\n for fp in file_paths:\n try:\n res = converter.convert(fp)\n ok = False\n if hasattr(res, \"status\"):\n try:\n ok = res.status == ConversionStatus.SUCCESS\n except Exception:\n ok = str(res.status).lower() == \"success\"\n if not ok and getattr(res, \"document\", None) is not None:\n ok = True\n if ok and res.document is not None:\n doc_json = res.document.export_to_dict()\n results.append({\n \"document\": doc_json,\n \"file_path\": str(fp),\n \"status\": \"SUCCESS\",\n })\n else:\n results.append(None)\n except Exception as e:\n sys.stderr.write(f\"Error processing {fp}: {e}\\n\")\n results.append(None)\n\n print(json.dumps({\"ok\": True, \"results\": results}))\n\n if __name__ == \"__main__\":\n main()\n \"\"\")\n\n def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]:\n # Check that docling is installed without actually importing it.\n # The real import (PyTorch, transformers, etc.) happens in the child\n # subprocess. Importing it here would spike memory and get the\n # Gunicorn worker SIGKILL'd by the OOM killer.\n import importlib.util\n\n if importlib.util.find_spec(\"docling\") is None:\n msg = (\n \"Docling is an optional dependency. Install with `uv pip install 'langflow[docling]'` or refer to the \"\n \"documentation on how to install optional dependencies.\"\n )\n raise ImportError(msg)\n\n file_paths = [str(file.path) for file in file_list if file.path]\n\n if not file_paths:\n self.log(\"No files to process.\")\n return file_list\n\n pic_desc_config: dict | None = None\n if self.pic_desc_llm is not None:\n pic_desc_config = _serialize_pydantic_model(self.pic_desc_llm)\n\n args = {\n \"file_paths\": file_paths,\n \"pipeline\": self.pipeline,\n \"ocr_engine\": self.ocr_engine,\n \"do_picture_classification\": self.do_picture_classification,\n \"pic_desc_config\": pic_desc_config,\n \"pic_desc_prompt\": self.pic_desc_prompt,\n }\n\n # Use Popen with a polling loop (same pattern as Read File advanced mode).\n # This avoids multiprocessing/threading issues under Gunicorn and keeps the\n # SSE event stream alive via periodic heartbeat logs.\n docling_timeout = 600 # 10 minutes\n poll_interval = 5\n\n # Use a temporary file for stdout to avoid pipe buffer deadlocks.\n # Docling (and its transitive imports: PyTorch, transformers, etc.) can\n # write large amounts of output. With subprocess.PIPE the OS pipe\n # buffer (~16 KB on macOS) fills up, the child blocks on write, and the\n # parent - which only reads *after* the child exits - waits forever.\n import tempfile\n\n with tempfile.TemporaryFile() as stdout_file, tempfile.TemporaryFile() as stderr_file:\n proc = subprocess.Popen( # noqa: S603\n [sys.executable, \"-u\", \"-c\", self._CHILD_SCRIPT],\n stdin=subprocess.PIPE,\n stdout=stdout_file,\n stderr=stderr_file,\n )\n proc.stdin.write(json.dumps(args).encode(\"utf-8\"))\n proc.stdin.close()\n\n start = time.monotonic()\n while proc.poll() is None:\n elapsed = time.monotonic() - start\n if elapsed >= docling_timeout:\n proc.kill()\n proc.wait()\n msg = (\n f\"Docling processing timed out after {docling_timeout}s. Try processing fewer or smaller files.\"\n )\n raise TimeoutError(msg)\n self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n time.sleep(poll_interval)\n\n stdout_file.seek(0)\n stderr_file.seek(0)\n stdout_bytes = stdout_file.read()\n stderr_bytes = stderr_file.read()\n\n if not stdout_bytes:\n err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output\"\n msg = f\"Docling subprocess error: {err_msg}\"\n raise RuntimeError(msg)\n\n try:\n payload = json.loads(stdout_bytes.decode(\"utf-8\"))\n except Exception as e:\n err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n msg = f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\"\n raise RuntimeError(msg) from e\n\n if not payload.get(\"ok\"):\n error_msg = payload.get(\"error\", \"Unknown Docling error\")\n if \"not installed\" in error_msg.lower():\n raise ImportError(error_msg)\n raise RuntimeError(error_msg)\n\n # Reconstruct DoclingDocument objects from JSON dicts returned by the child\n from docling_core.types.doc import DoclingDocument\n\n raw_results = payload.get(\"results\", [])\n processed_data: list[Data | None] = []\n for r in raw_results:\n if r is None:\n processed_data.append(None)\n continue\n try:\n doc = DoclingDocument.model_validate(r[\"document\"])\n except Exception: # noqa: BLE001\n # Fall back to keeping the raw dict if validation fails\n doc = r[\"document\"]\n processed_data.append(Data(data={\"doc\": doc, \"file_path\": r[\"file_path\"]}))\n\n return self.rollup_data(file_list, processed_data)\n" + "value": "import json\nimport subprocess\nimport sys\nimport textwrap\nimport time\n\nfrom lfx.base.data import BaseFileComponent\nfrom lfx.base.data.docling_utils import _serialize_pydantic_model\nfrom lfx.inputs import BoolInput, DropdownInput, HandleInput, StrInput\nfrom lfx.schema import Data\n\n\nclass DoclingInlineComponent(BaseFileComponent):\n display_name = \"Docling\"\n description = \"Uses Docling to process input documents running the Docling models locally.\"\n documentation = \"https://docling-project.github.io/docling/\"\n trace_type = \"tool\"\n icon = \"Docling\"\n name = \"DoclingInline\"\n\n # https://docling-project.github.io/docling/usage/supported_formats/\n VALID_EXTENSIONS = [\n \"adoc\",\n \"asciidoc\",\n \"asc\",\n \"bmp\",\n \"csv\",\n \"dotx\",\n \"dotm\",\n \"docm\",\n \"docx\",\n \"htm\",\n \"html\",\n \"jpeg\",\n \"json\",\n \"md\",\n \"pdf\",\n \"png\",\n \"potx\",\n \"ppsx\",\n \"pptm\",\n \"potm\",\n \"ppsm\",\n \"pptx\",\n \"tiff\",\n \"txt\",\n \"xls\",\n \"xlsx\",\n \"xhtml\",\n \"xml\",\n \"webp\",\n ]\n\n inputs = [\n *BaseFileComponent.get_base_inputs(),\n DropdownInput(\n name=\"pipeline\",\n display_name=\"Pipeline\",\n info=\"Docling pipeline to use\",\n options=[\"standard\", \"vlm\"],\n value=\"standard\",\n ),\n DropdownInput(\n name=\"ocr_engine\",\n display_name=\"OCR Engine\",\n info=\"OCR engine to use. None will disable OCR.\",\n options=[\"None\", \"easyocr\", \"tesserocr\", \"rapidocr\", \"ocrmac\"],\n value=\"None\",\n ),\n BoolInput(\n name=\"do_picture_classification\",\n display_name=\"Picture classification\",\n info=\"If enabled, the Docling pipeline will classify the pictures type.\",\n value=False,\n ),\n HandleInput(\n name=\"pic_desc_llm\",\n display_name=\"Picture description LLM\",\n info=\"If connected, the model to use for running the picture description task.\",\n input_types=[\"LanguageModel\"],\n required=False,\n ),\n StrInput(\n name=\"pic_desc_prompt\",\n display_name=\"Picture description prompt\",\n value=\"Describe the image in three sentences. Be concise and accurate.\",\n info=\"The user prompt to use when invoking the model.\",\n advanced=True,\n ),\n # TODO: expose more Docling options\n ]\n\n outputs = [\n *BaseFileComponent.get_base_outputs(),\n ]\n\n # ------------------------------------------------------------------ #\n # Child script that runs Docling in a separate OS process. #\n # Uses subprocess.Popen (same pattern as Read File advanced mode) #\n # instead of multiprocessing/threading so that: #\n # 1. It works reliably under Gunicorn's fork-based workers #\n # 2. The parent's event loop stays free for SSE heartbeats #\n # 3. No pickling / signal-handler conflicts #\n # ------------------------------------------------------------------ #\n _CHILD_SCRIPT: str = textwrap.dedent(r\"\"\"\n import json, sys\n\n def main():\n cfg = json.loads(sys.stdin.read())\n file_paths = cfg[\"file_paths\"]\n pipeline = cfg[\"pipeline\"]\n ocr_engine = cfg[\"ocr_engine\"]\n do_picture_cls = cfg[\"do_picture_classification\"]\n pic_desc_config = cfg.get(\"pic_desc_config\")\n pic_desc_prompt = cfg.get(\"pic_desc_prompt\", \"\")\n\n try:\n from docling.datamodel.base_models import ConversionStatus, InputFormat\n from docling.datamodel.pipeline_options import PdfPipelineOptions\n from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption\n except ImportError as e:\n print(json.dumps({\"ok\": False, \"error\": f\"Docling is not installed: {e}\"}))\n return\n\n # --- build converter ------------------------------------------------\n try:\n pipe = PdfPipelineOptions()\n pipe.do_ocr = ocr_engine not in (\"\", \"None\")\n if pipe.do_ocr:\n try:\n from docling.models.factories import get_ocr_factory\n fac = get_ocr_factory(allow_external_plugins=False)\n pipe.ocr_options = fac.create_options(kind=ocr_engine)\n except Exception:\n pipe.do_ocr = False\n\n pipe.do_picture_classification = do_picture_cls\n\n if pic_desc_config:\n try:\n import importlib\n from pydantic import TypeAdapter\n try:\n from langchain_docling.picture_description import PictureDescriptionLangChainOptions\n except ImportError as e:\n print(json.dumps({\n \"ok\": False,\n \"error\": (\n \"langchain-docling is not installed. Please install it with \"\n \"`pip install langchain-docling` or \"\n \"`pip install 'langflow[docling-image-description]'`.\"\n )\n }))\n return\n mod_name, cls_name = pic_desc_config[\"__class_path__\"].rsplit(\".\", 1)\n mod = importlib.import_module(mod_name)\n cls = getattr(mod, cls_name)\n adapter = TypeAdapter(cls)\n llm = adapter.validate_python(pic_desc_config[\"config\"])\n pipe.do_picture_description = True\n pipe.allow_external_plugins = True\n pipe.picture_description_options = PictureDescriptionLangChainOptions(\n llm=llm, prompt=pic_desc_prompt,\n )\n except Exception as e:\n print(json.dumps({\"ok\": False, \"error\": f\"Picture description setup failed: {e}\"}))\n return\n\n\n if pipeline == \"vlm\":\n try:\n from docling.datamodel.pipeline_options import VlmPipelineOptions\n from docling.pipeline.vlm_pipeline import VlmPipeline\n vlm_opts = VlmPipelineOptions()\n if sys.platform == \"darwin\":\n try:\n from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX\n vlm_opts.vlm_options = GRANITEDOCLING_MLX\n except ImportError:\n from docling.datamodel.vlm_model_specs import GRANITEDOCLING_TRANSFORMERS\n vlm_opts.vlm_options = GRANITEDOCLING_TRANSFORMERS\n fmt = {}\n if hasattr(InputFormat, \"PDF\"):\n fmt[InputFormat.PDF] = PdfFormatOption(\n pipeline_cls=VlmPipeline, pipeline_options=vlm_opts,\n )\n if hasattr(InputFormat, \"IMAGE\"):\n fmt[InputFormat.IMAGE] = PdfFormatOption(\n pipeline_cls=VlmPipeline, pipeline_options=vlm_opts,\n )\n converter = DocumentConverter(format_options=fmt)\n except Exception as e:\n print(json.dumps({\"ok\": False, \"error\": f\"VLM pipeline setup failed: {e}\"}))\n return\n else:\n pdf_opt = PdfFormatOption(pipeline_options=pipe)\n fmt = {}\n if hasattr(InputFormat, \"PDF\"):\n fmt[InputFormat.PDF] = pdf_opt\n if hasattr(InputFormat, \"IMAGE\"):\n fmt[InputFormat.IMAGE] = pdf_opt\n converter = DocumentConverter(format_options=fmt)\n except Exception as e:\n print(json.dumps({\"ok\": False, \"error\": f\"Converter creation failed: {e}\"}))\n return\n\n # --- process files --------------------------------------------------\n results = []\n for fp in file_paths:\n try:\n res = converter.convert(fp)\n ok = False\n if hasattr(res, \"status\"):\n try:\n ok = res.status == ConversionStatus.SUCCESS\n except Exception:\n ok = str(res.status).lower() == \"success\"\n if not ok and getattr(res, \"document\", None) is not None:\n ok = True\n if ok and res.document is not None:\n doc_json = res.document.export_to_dict()\n results.append({\n \"document\": doc_json,\n \"file_path\": str(fp),\n \"status\": \"SUCCESS\",\n })\n else:\n results.append(None)\n except Exception as e:\n sys.stderr.write(f\"Error processing {fp}: {e}\\n\")\n results.append(None)\n\n print(json.dumps({\"ok\": True, \"results\": results}))\n\n if __name__ == \"__main__\":\n main()\n \"\"\")\n\n def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]:\n # Check that docling is installed without actually importing it.\n # The real import (PyTorch, transformers, etc.) happens in the child\n # subprocess. Importing it here would spike memory and get the\n # Gunicorn worker SIGKILL'd by the OOM killer.\n import importlib.util\n\n if importlib.util.find_spec(\"docling\") is None:\n msg = (\n \"Docling is an optional dependency. Install with `uv pip install 'langflow[docling]'` or refer to the \"\n \"documentation on how to install optional dependencies.\"\n )\n raise ImportError(msg)\n\n file_paths = [str(file.path) for file in file_list if file.path]\n\n if not file_paths:\n self.log(\"No files to process.\")\n return file_list\n\n pic_desc_config: dict | None = None\n if self.pic_desc_llm is not None:\n pic_desc_config = _serialize_pydantic_model(self.pic_desc_llm)\n\n args = {\n \"file_paths\": file_paths,\n \"pipeline\": self.pipeline,\n \"ocr_engine\": self.ocr_engine,\n \"do_picture_classification\": self.do_picture_classification,\n \"pic_desc_config\": pic_desc_config,\n \"pic_desc_prompt\": self.pic_desc_prompt,\n }\n\n # Use Popen with a polling loop (same pattern as Read File advanced mode).\n # This avoids multiprocessing/threading issues under Gunicorn and keeps the\n # SSE event stream alive via periodic heartbeat logs.\n docling_timeout = 600 # 10 minutes\n poll_interval = 5\n\n # Use a temporary file for stdout to avoid pipe buffer deadlocks.\n # Docling (and its transitive imports: PyTorch, transformers, etc.) can\n # write large amounts of output. With subprocess.PIPE the OS pipe\n # buffer (~16 KB on macOS) fills up, the child blocks on write, and the\n # parent - which only reads *after* the child exits - waits forever.\n import tempfile\n\n with tempfile.TemporaryFile() as stdout_file, tempfile.TemporaryFile() as stderr_file:\n proc = subprocess.Popen( # noqa: S603\n [sys.executable, \"-u\", \"-c\", self._CHILD_SCRIPT],\n stdin=subprocess.PIPE,\n stdout=stdout_file,\n stderr=stderr_file,\n )\n proc.stdin.write(json.dumps(args).encode(\"utf-8\"))\n proc.stdin.close()\n\n start = time.monotonic()\n while proc.poll() is None:\n elapsed = time.monotonic() - start\n if elapsed >= docling_timeout:\n proc.kill()\n proc.wait()\n msg = (\n f\"Docling processing timed out after {docling_timeout}s. Try processing fewer or smaller files.\"\n )\n raise TimeoutError(msg)\n self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n time.sleep(poll_interval)\n\n stdout_file.seek(0)\n stderr_file.seek(0)\n stdout_bytes = stdout_file.read()\n stderr_bytes = stderr_file.read()\n\n if not stdout_bytes:\n err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output\"\n msg = f\"Docling subprocess error: {err_msg}\"\n raise RuntimeError(msg)\n\n try:\n payload = json.loads(stdout_bytes.decode(\"utf-8\"))\n except Exception as e:\n err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n msg = f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\"\n raise RuntimeError(msg) from e\n\n if not payload.get(\"ok\"):\n error_msg = payload.get(\"error\", \"Unknown Docling error\")\n if \"not installed\" in error_msg.lower():\n raise ImportError(error_msg)\n raise RuntimeError(error_msg)\n\n # Reconstruct DoclingDocument objects from JSON dicts returned by the child\n from docling_core.types.doc import DoclingDocument\n\n raw_results = payload.get(\"results\", [])\n processed_data: list[Data | None] = []\n for r in raw_results:\n if r is None:\n processed_data.append(None)\n continue\n try:\n doc = DoclingDocument.model_validate(r[\"document\"])\n except Exception: # noqa: BLE001\n # Fall back to keeping the raw dict if validation fails\n doc = r[\"document\"]\n processed_data.append(Data(data={\"doc\": doc, \"file_path\": r[\"file_path\"]}))\n\n return self.rollup_data(file_list, processed_data)\n" }, "delete_server_file_after_processing": { "_input_type": "BoolInput", @@ -89174,7 +89174,7 @@ "dependencies": [ { "name": "mem0", - "version": "2.0.2" + "version": "2.0.3" }, { "name": "lfx", @@ -98081,7 +98081,7 @@ "dependencies": [ { "name": "langchain_perplexity", - "version": "1.2.0" + "version": "1.3.0" }, { "name": "pydantic", @@ -117883,6 +117883,6 @@ "num_components": 354, "num_modules": 97 }, - "sha256": "3de2ff959fce3954f17d7edb441400334a577074dd2cf70ef31e82c12f7e5b67", - "version": "0.4.4" + "sha256": "30d28b29f1d40a6a3806ed49e4193709d6645a90dca3da7a8ef18da708fb8b7b", + "version": "0.4.5" } diff --git a/src/lfx/src/lfx/base/data/docling_utils.py b/src/lfx/src/lfx/base/data/docling_utils.py index 7a96e3a61e..9f9fd814b7 100644 --- a/src/lfx/src/lfx/base/data/docling_utils.py +++ b/src/lfx/src/lfx/base/data/docling_utils.py @@ -292,7 +292,6 @@ def docling_worker( from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption # noqa: F401 from docling.models.factories import get_ocr_factory # noqa: F401 from docling.pipeline.vlm_pipeline import VlmPipeline # noqa: F401 - from langchain_docling.picture_description import PictureDescriptionLangChainOptions # noqa: F401 # Check for shutdown after imports check_shutdown() @@ -332,7 +331,6 @@ def docling_worker( from docling.datamodel.pipeline_options import PdfPipelineOptions from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption from docling.models.factories import get_ocr_factory - from langchain_docling.picture_description import PictureDescriptionLangChainOptions pipeline_options = PdfPipelineOptions() pipeline_options.do_ocr = ocr_engine not in {"", "None"} @@ -342,6 +340,14 @@ def docling_worker( pipeline_options.ocr_options = ocr_options pipeline_options.do_picture_classification = do_picture_classification + try: + from langchain_docling.picture_description import PictureDescriptionLangChainOptions + except ImportError as e: + msg = ( + "langchain-docling is not installed. Please install it with `pip install langchain-docling` " + "or `pip install 'langflow[docling-image-description]'`." + ) + raise ImportError(msg) from e pic_desc_llm = _deserialize_pydantic_model(pic_desc_config) logger.info("Docling enabling the picture description stage.") pipeline_options.do_picture_description = True diff --git a/src/lfx/src/lfx/components/docling/chunk_docling_document.py b/src/lfx/src/lfx/components/docling/chunk_docling_document.py index 4137bf75f3..e569d82000 100644 --- a/src/lfx/src/lfx/components/docling/chunk_docling_document.py +++ b/src/lfx/src/lfx/components/docling/chunk_docling_document.py @@ -1,14 +1,27 @@ import json - -import tiktoken -from docling_core.transforms.chunker import BaseChunker, DocMeta -from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker +from typing import Any from lfx.base.data.docling_utils import extract_docling_documents from lfx.custom import Component from lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MessageTextInput, Output, StrInput from lfx.schema import Data, DataFrame +_CHUNKING_INSTALL_HINT = ( + "Install them with `uv pip install 'langflow[docling-chunking]'`, " + "`uv pip install 'langflow-base[docling-chunking]'`, or " + "`uv pip install 'docling-core[chunking]' tiktoken`." +) + + +def _load_docling_chunker_dependencies() -> tuple[type[Any], type[Any]]: + try: + from docling_core.transforms.chunker.doc_chunk import DocMeta as DocMetaCls + from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker as HierarchicalChunkerCls + except (ImportError, RuntimeError) as e: + msg = f"Docling chunking dependencies are not installed. {_CHUNKING_INSTALL_HINT}" + raise ImportError(msg) from e + return DocMetaCls, HierarchicalChunkerCls + class ChunkDoclingDocumentComponent(Component): display_name: str = "Chunk DoclingDocument" @@ -144,25 +157,20 @@ class ChunkDoclingDocumentComponent(Component): if warning: self.status = warning - chunker: BaseChunker + doc_meta_cls, hierarchical_chunker_cls = _load_docling_chunker_dependencies() + chunker: Any if self.chunker == "HybridChunker": try: from docling_core.transforms.chunker.hybrid_chunker import HybridChunker - except ImportError as e: - msg = ( - "HybridChunker is not installed. Please install it with `uv pip install docling-core[chunking] " - "or `uv pip install transformers`" - ) + except (ImportError, RuntimeError) as e: + msg = f"HybridChunker is not installed. {_CHUNKING_INSTALL_HINT}" raise ImportError(msg) from e max_tokens: int | None = self.max_tokens if self.max_tokens else None if self.provider == "Hugging Face": try: from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer - except ImportError as e: - msg = ( - "HuggingFaceTokenizer is not installed." - " Please install it with `uv pip install docling-core[chunking]`" - ) + except (ImportError, RuntimeError) as e: + msg = f"HuggingFaceTokenizer is not installed. {_CHUNKING_INSTALL_HINT}" raise ImportError(msg) from e tokenizer = HuggingFaceTokenizer.from_pretrained( model_name=self.hf_model_name, @@ -170,13 +178,10 @@ class ChunkDoclingDocumentComponent(Component): ) elif self.provider == "OpenAI": try: + import tiktoken from docling_core.transforms.chunker.tokenizer.openai import OpenAITokenizer - except ImportError as e: - msg = ( - "OpenAITokenizer is not installed." - " Please install it with `uv pip install docling-core[chunking]`" - " or `uv pip install transformers`" - ) + except (ImportError, RuntimeError) as e: + msg = f"OpenAITokenizer is not installed. {_CHUNKING_INSTALL_HINT}" raise ImportError(msg) from e if max_tokens is None: max_tokens = 128 * 1024 # context window length required for OpenAI tokenizers @@ -190,7 +195,7 @@ class ChunkDoclingDocumentComponent(Component): ) elif self.chunker == "HierarchicalChunker": - chunker = HierarchicalChunker() + chunker = hierarchical_chunker_cls() else: msg = f"Unknown chunker: {self.chunker}" raise ValueError(msg) @@ -200,7 +205,7 @@ class ChunkDoclingDocumentComponent(Component): for doc in documents: for chunk in chunker.chunk(dl_doc=doc): enriched_text = chunker.contextualize(chunk=chunk) - meta = DocMeta.model_validate(chunk.meta) + meta = doc_meta_cls.model_validate(chunk.meta) results.append( Data( diff --git a/src/lfx/src/lfx/components/docling/docling_inline.py b/src/lfx/src/lfx/components/docling/docling_inline.py index ffcbaa5e02..d1b5e50f45 100644 --- a/src/lfx/src/lfx/components/docling/docling_inline.py +++ b/src/lfx/src/lfx/components/docling/docling_inline.py @@ -140,9 +140,18 @@ class DoclingInlineComponent(BaseFileComponent): try: import importlib from pydantic import TypeAdapter - from langchain_docling.picture_description import ( - PictureDescriptionLangChainOptions, - ) + try: + from langchain_docling.picture_description import PictureDescriptionLangChainOptions + except ImportError as e: + print(json.dumps({ + "ok": False, + "error": ( + "langchain-docling is not installed. Please install it with " + "`pip install langchain-docling` or " + "`pip install 'langflow[docling-image-description]'`." + ) + })) + return mod_name, cls_name = pic_desc_config["__class_path__"].rsplit(".", 1) mod = importlib.import_module(mod_name) cls = getattr(mod, cls_name) @@ -157,6 +166,7 @@ class DoclingInlineComponent(BaseFileComponent): print(json.dumps({"ok": False, "error": f"Picture description setup failed: {e}"})) return + if pipeline == "vlm": try: from docling.datamodel.pipeline_options import VlmPipelineOptions diff --git a/uv.lock b/uv.lock index 52cc5b63f1..cf178f8af6 100644 --- a/uv.lock +++ b/uv.lock @@ -3023,6 +3023,61 @@ wheels = [ ] [package.optional-dependencies] +cli = [ + { name = "rich" }, + { name = "typer" }, +] +convert-core = [ + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "pillow" }, + { name = "rtree" }, + { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, +] +extract-core = [ + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "pillow" }, + { name = "polyfactory" }, + { name = "rtree" }, + { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, +] +feat-ocr-rapidocr = [ + { name = "rapidocr" }, +] +format-latex = [ + { name = "pylatexenc" }, +] +format-office = [ + { name = "openpyxl" }, + { name = "python-docx" }, + { name = "python-pptx" }, +] +format-pdf = [ + { name = "docling-parse" }, + { name = "pypdfium2" }, +] +format-web = [ + { name = "beautifulsoup4" }, + { name = "lxml" }, + { name = "marko" }, +] +models-local = [ + { name = "accelerate" }, + { name = "defusedxml" }, + { name = "docling-ibm-models" }, + { name = "huggingface-hub" }, + { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and sys_platform == 'darwin') or (platform_machine == 'arm64' and sys_platform == 'darwin')" }, + { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_machine != 'arm64') or sys_platform != 'darwin'" }, + { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and sys_platform == 'darwin') or (platform_machine == 'arm64' and sys_platform == 'darwin')" }, + { name = "torchvision", version = "0.27.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_machine != 'arm64') or sys_platform != 'darwin'" }, +] +service-client = [ + { name = "httpx" }, + { name = "websockets" }, +] standard = [ { name = "accelerate" }, { name = "beautifulsoup4" }, @@ -4241,10 +4296,10 @@ name = "gassist" version = "0.0.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "python_full_version >= '3.14' or sys_platform != 'darwin'" }, - { name = "flask", marker = "python_full_version >= '3.14' or sys_platform != 'darwin'" }, - { name = "flask-cors", marker = "python_full_version >= '3.14' or sys_platform != 'darwin'" }, - { name = "tqdm", marker = "python_full_version >= '3.14' or sys_platform != 'darwin'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "flask", marker = "sys_platform == 'win32'" }, + { name = "flask-cors", marker = "sys_platform == 'win32'" }, + { name = "tqdm", marker = "sys_platform == 'win32'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/b0/2e/f79632d7300874f7f0e60b61a6ab22455a245e1556116a1729542a77b0da/gassist-0.0.1-py3-none-any.whl", hash = "sha256:bb0fac74b453153a6c74b2db40a14fdde7879cbc10ec692ed170e576c8e2b6aa", size = 23819, upload-time = "2025-05-09T18:22:23.609Z" }, @@ -6934,7 +6989,7 @@ couchbase = [ { name = "couchbase" }, ] docling = [ - { name = "langchain-docling" }, + { name = "langflow-base", extra = ["docling"] }, { name = "ocrmac", marker = "sys_platform == 'darwin'" }, { name = "rapidocr-onnxruntime" }, { name = "tesserocr" }, @@ -6943,6 +6998,13 @@ docling = [ { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.14' and sys_platform == 'darwin') or (platform_machine == 'arm64' and sys_platform == 'darwin')" }, { name = "torchvision", version = "0.27.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.14' and platform_machine != 'arm64') or sys_platform != 'darwin'" }, ] +docling-chunking = [ + { name = "langflow-base", extra = ["docling-chunking"] }, +] +docling-image-description = [ + { name = "langchain-docling" }, + { name = "langflow-base", extra = ["docling-image-description"] }, +] local = [ { name = "ctransformers" }, { name = "sentence-transformers" }, @@ -7003,8 +7065,11 @@ requires-dist = [ { name = "clickhouse-connect", marker = "extra == 'clickhouse-connect'", specifier = "==0.7.19" }, { name = "couchbase", marker = "extra == 'couchbase'", specifier = ">=4.2.1" }, { name = "ctransformers", marker = "extra == 'local'", specifier = ">=0.2.10" }, - { name = "langchain-docling", marker = "extra == 'docling'", specifier = ">=1.1.0" }, + { name = "langchain-docling", marker = "extra == 'docling-image-description'", specifier = ">=1.1.0" }, { name = "langflow-base", extras = ["complete"], editable = "src/backend/base" }, + { name = "langflow-base", extras = ["docling"], marker = "extra == 'docling'", editable = "src/backend/base" }, + { name = "langflow-base", extras = ["docling-chunking"], marker = "extra == 'docling-chunking'", editable = "src/backend/base" }, + { name = "langflow-base", extras = ["docling-image-description"], marker = "extra == 'docling-image-description'", editable = "src/backend/base" }, { name = "nv-ingest-api", marker = "python_full_version >= '3.12' and extra == 'nv-ingest'", specifier = ">=26.1.0,<27.0.0" }, { name = "nv-ingest-client", marker = "python_full_version >= '3.12' and extra == 'nv-ingest'", specifier = ">=26.1.0,<27.0.0" }, { name = "ocrmac", marker = "sys_platform == 'darwin' and extra == 'docling'", specifier = ">=1.0.0" }, @@ -7017,7 +7082,7 @@ requires-dist = [ { name = "torchvision", marker = "extra == 'docling'", specifier = ">=0.21.0", index = "https://download.pytorch.org/whl/cpu" }, { name = "webrtcvad", marker = "extra == 'audio'", specifier = ">=2.0.10" }, ] -provides-extras = ["docling", "audio", "couchbase", "cassio", "local", "clickhouse-connect", "nv-ingest", "postgresql"] +provides-extras = ["docling", "docling-chunking", "docling-image-description", "audio", "couchbase", "cassio", "local", "clickhouse-connect", "nv-ingest", "postgresql"] [package.metadata.requires-dev] dev = [ @@ -7190,8 +7255,8 @@ all = [ { name = "cuga", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" }, { name = "datasets" }, { name = "ddgs" }, - { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "docling-core" }, + { name = "docling-slim", extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "easyocr", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "elasticsearch" }, { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" }, @@ -7359,8 +7424,8 @@ complete = [ { name = "cuga", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" }, { name = "datasets" }, { name = "ddgs" }, - { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "docling-core" }, + { name = "docling-slim", extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "easyocr", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "elasticsearch" }, { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" }, @@ -7472,8 +7537,18 @@ datasets = [ { name = "datasets" }, ] docling = [ - { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "docling-core" }, + { name = "docling-slim", extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, +] +docling-chunking = [ + { name = "docling-core", extra = ["chunking"] }, + { name = "docling-slim", extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, + { name = "tiktoken" }, +] +docling-image-description = [ + { name = "docling-core" }, + { name = "docling-slim", extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, + { name = "langchain-docling" }, ] docx = [ { name = "python-docx" }, @@ -7794,8 +7869,9 @@ requires-dist = [ { name = "datasets", marker = "extra == 'datasets'", specifier = ">2.14.7,<5.0.0" }, { name = "ddgs", marker = "extra == 'duckduckgo'", specifier = ">=9.0.0" }, { name = "defusedxml", specifier = ">=0.7.1,<1.0.0" }, - { name = "docling", marker = "(platform_machine != 'x86_64' and extra == 'docling') or (sys_platform != 'darwin' and extra == 'docling')", specifier = ">=2.36.1,<3.0.0" }, { name = "docling-core", marker = "extra == 'docling'", specifier = ">=2.77.0,<3.0.0" }, + { name = "docling-core", extras = ["chunking"], marker = "extra == 'docling-chunking'", specifier = ">=2.77.0,<3.0.0" }, + { name = "docling-slim", extras = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "(platform_machine != 'x86_64' and extra == 'docling') or (sys_platform != 'darwin' and extra == 'docling')", specifier = ">=2.36.1,<3.0.0" }, { name = "docstring-parser", specifier = ">=0.16,<1.0.0" }, { name = "duckdb", specifier = ">=1.0.0,<2.0.0" }, { name = "dynaconf", specifier = ">=3.2.13,<4.0.0" }, @@ -7847,6 +7923,7 @@ requires-dist = [ { name = "langchain-cohere", marker = "extra == 'cohere'", specifier = "~=0.5.0" }, { name = "langchain-community", specifier = "~=0.4.1" }, { name = "langchain-core", specifier = ">=1.3.3,<2.0.0" }, + { name = "langchain-docling", marker = "extra == 'docling-image-description'", specifier = ">=1.1.0" }, { name = "langchain-elasticsearch", marker = "extra == 'elasticsearch'", specifier = "~=1.0.0" }, { name = "langchain-experimental", specifier = "~=0.4.1" }, { name = "langchain-google-calendar-tools", marker = "extra == 'google'", specifier = "~=0.0.1" }, @@ -7896,6 +7973,8 @@ requires-dist = [ { name = "langflow-base", extras = ["cuga"], marker = "extra == 'complete'", editable = "src/backend/base" }, { name = "langflow-base", extras = ["datasets"], marker = "extra == 'complete'", editable = "src/backend/base" }, { name = "langflow-base", extras = ["docling"], marker = "extra == 'complete'", editable = "src/backend/base" }, + { name = "langflow-base", extras = ["docling"], marker = "extra == 'docling-chunking'", editable = "src/backend/base" }, + { name = "langflow-base", extras = ["docling"], marker = "extra == 'docling-image-description'", editable = "src/backend/base" }, { name = 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